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2005, vol. 2, br. 5, str. 485-492
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Modeliranje prečistača gasova primenom neuronske mreže
The modeling of air pollution control devices using neural networks
Univerzitet u Nišu, Fakultet zaštite na radu, Srbija
Sažetak
Većina metoda za prečišćavanje gasova podrazumeva prolazak gasne struje kroz neki sistem za prečišćavanje. Takav sistem se uglavnom sastoji od različitih uređaja za prečišćavanje. Izabrani sistem i njegove karakteristike zavise o kakvom se zagađenju radi, dali su to aerosoli, čestice, raspršene kapi ili gasovi. Svakako da noseći gas,proces emisije i promene u izvoru zagađenja utiču na izbor sistema za prečišćavanje. Postoji veliki broj parametara koje treba razmotriti u procesu izbora sredstava i sistema kontrole, a ova studija predstavlja njihovo modeliranje. Osnovni zadatak je dobijanje modela nepoznate, vremenski promenljive nelinearne zavisnosti. Predložen je algoritam za sekvencijalnu adaptaciju mreže radijalnih bazisnih funkcija (RBF). Adaptacija parametara i strukture je inkorporirana u sistem proširenog Kalmanovog filtra. Za vreme adaptacije strukture RBF mreže kombinovana su dva prilaza: izgradnja (rast) i uprošćenje.
Abstract
The majority of methods for pollutant elimination assume the flow of the polluted gas through the pollution control system. The system is made of various devices which have to be chosen based on the characteristics of the pollutant: aerosol, solid particles, droplets or gaseous. The chosen framework and facilities depend on the type of the pollutant: aerosol, solid particles, droplets or gaseous. There are a number of basic parameters which have to be considered in order to define air pollution control devices. This study represents a modeling of the named parameters which are related to the framework and facilities of air pollution control. In order to set the optimal parameters of a purification device, a deterministic model of the process of purification should be determined. Such a model is often difficult to construct, since physical and chemical characteristics of the source of pollution are not completely known. In this paper we propose a black-box modeling tool based on the application of an artificial neural network.
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